def load_results(exp_cfgs):
exp_cfgs = exp_cfgs[0] + exp_cfgs[1]
sources, targets = {}, {}
for i, exp_cfg in enumerate(exp_cfgs):
cwd = os.getcwd()
batch = jobfactory.make_jobgroup([exp_cfg])
os.chdir(os.path.expanduser(exp_cfg.meta.rootpath))
results_hub = hub.ResultsHub(batch, kind='cov')
src_data = results_hub.data()
assert len(src_data) == 1
results = {'exp_cfg': exp_cfg}
results.update(src_data[0])
if len(exp_cfg.exp.key[1]) == 0:
sources[(exp_cfg.exp.env_name,
exp_cfg.exp.explorer_name)] = results
else:
if len(src_data[0]['avg']) == 0:
print('MISSING: {}'.format(exp_cfg.exp.key))
else:
targets[exp_cfg.exp.key] = results
os.chdir(cwd)
return sources, targets
def load_data(kind):
cwd = os.getcwd()
Ns = [1000, 2000, 5000, 10000]
ps, avgs, stds = ({(env, d, N): [] for env in env_names for d in disturbs for N in Ns},
{(env, d, N): {} for env in env_names for d in disturbs for N in Ns},
{(env, d, N): {} for env in env_names for d in disturbs for N in Ns})
ds = [0.001, 0.05, 0.5]
env_keys = []
min_avgs, min_stds = {}, {}
for i, exp_cfg in enumerate(exp_cfgs):
if exp_cfg.exploration.explorer.ex_1.learner.m_disturb in ds:
batch = jobfactory.make_jobgroup([exp_cfg])
os.chdir(os.path.expanduser(exp_cfg.meta.rootpath))
results_hub = hub.ResultsHub(batch, kind=kind)
data = results_hub.data()[0]
d = exp_cfg.exploration.explorer.ex_1.learner.m_disturb
p = exp_cfg.exploration.explorer.weights[1][0]
for N in Ns:
env = exp_cfg.exp.env_name
index = data['ticks'].index(N)
env_key = (env, d, N)
ps[env_key].append(p)
avgs[env_key][p] = data['avg'][index]
stds[env_key][p] = data['std'][index]
if p == 0:
env_keys.append(env_key)
for t in data['ticks']:
env_name = exp_cfg.exp.env_name
env_key = (env_name, d, t)
min_avgs.setdefault(env_key, float('inf'))
index = data['ticks'].index(t)
if min_avgs[env_key] >= data['avg'][index]:
min_avgs[env_key] = data['avg'][index]
min_stds[env_key] = data['std'][index]
for env_key in env_keys:
ps[env_key] = sorted(ps[env_key])
avgs[env_key] = np.array([avgs[env_key][p] for p in ps[env_key]])
stds[env_key] = np.array([stds[env_key][p] for p in ps[env_key]])
os.chdir(cwd)
return env_keys, ps, avgs, stds, min_avgs, min_stds
from fig3_8_unreach_graph import ratios, avgs, stds
from figB_4_adapt_reach_grid_cluster import expcfgs_levels, windows, RESS
#Ns = [100, 1000, 2000, 5000, 10000]
Ns = [2000, 10000]
W = windows[0]
cwd = os.getcwd()
adapt_avgs = {}
adapt_stds = {}
for i, exp_cfg in enumerate(expcfgs_levels[0]):
batch = jobfactory.make_jobgroup([exp_cfg])
os.chdir(os.path.expanduser(exp_cfg.meta.rootpath))
data = hub.ResultsHub(batch, kind='nn').data()[0]
window = exp_cfg.exploration.explorer.ex_1.window
res = exp_cfg.exploration.explorer.ex_1.res
for N in Ns:
print(data['ticks'])
index = data['ticks'].index(N)
adapt_avgs[(window, N, res)] = data['avg'][index]
adapt_stds[(window, N, res)] = data['std'][index]
os.chdir(cwd)
# for N in Ns:
# for w in windows:
# print('N={}, w={} : {} +- {}'.format(N, w, adapt_avgs[(w, N, 20)], adapt_stds[(w, N, 20)]))
# print()
ticks = [2000, 10000]
ratios_ = range(0, 101, 5)
ratios, avgs, stds = {tick: []
for tick in ticks}, {tick: []
for tick in ticks
}, {tick: []
for tick in ticks}
y_max = 0.0
cwd = os.getcwd()
for i, exp_cfg in enumerate(exp_cfgs):
batch = jobfactory.make_jobgroup([exp_cfg])
os.chdir(os.path.expanduser(exp_cfg.meta.rootpath))
results_hub = hub.ResultsHub(batch, kind='nn')
src_data = results_hub.data()
assert len(src_data) == 1
for tick in ticks:
try:
index = src_data[0]['ticks'].index(tick)
ratios[tick].append(ratios_[i])
avgs[tick].append(src_data[0]['avg'][index])
stds[tick].append(src_data[0]['std'][index])
y_max = max(y_max, max(avgs[tick]) + max(stds[tick]))
except ValueError:
pass
os.chdir(cwd)
RESS = [20, 40]
Ns = [10000]
KIND = 'nn'
mbratio_env_keys, ps, avgs, stds, min_avgs, min_stds = load_data(KIND)
cwd = os.getcwd()
adapt_avgs = {}
adapt_stds = {}
for i, exp_cfg in enumerate(expcfgs_levels[0]):
batch = jobfactory.make_jobgroup([exp_cfg])
os.chdir(os.path.expanduser(exp_cfg.meta.rootpath))
data = hub.ResultsHub(batch, kind=KIND).data()[0]
res = exp_cfg.exploration.explorer.res
d = exp_cfg.exploration.explorer.ex_1.learner.m_disturb
# for N in cfg.testsnn.ticks:
for N in Ns:
index = data['ticks'].index(N)
adapt_avgs[(d, res, N)] = data['avg'][index]
adapt_stds[(d, res, N)] = data['std'][index]
os.chdir(cwd)
# for d in disturbs:
# for N in Ns:
# print('N={}: {} +- {}'.format(N, adapt_avgs[(d, res, N)], adapt_stds[(d, res N)]))
dims, avgs, stds = {}, {}, {}
y_max = 0.0
cwd = os.getcwd()
displayed_dims = [2, 5, 7, 10, 15, 20, 30, 40, 60, 80,
100] #, 500, 1000]#, 2000]
for i, exp_cfg in enumerate(expcfgs_levels[0]):
dim = exp_cfg.job.env.dim
if dim in displayed_dims:
batch = jobfactory.make_jobgroup([exp_cfg])
os.chdir(os.path.expanduser(exp_cfg.meta.rootpath))
results_hub = hub.ResultsHub(batch, kind='cov')
data = results_hub.data()[0]
ex_name = exp_cfg.exp.explorer_name
dims.setdefault(ex_name, [])
avgs.setdefault(ex_name, [])
stds.setdefault(ex_name, [])
dims[ex_name].append(dim)
avgs[ex_name].append(data['avg'][-1])
stds[ex_name].append(data['std'][-1])
y_max = max(y_max, data['avg'][-1] + data['std'][-1])
os.chdir(cwd)
colors = ['#2577B2', '#E84A5F']